Building a Semantic Data Layer for RAG Pipelines

Retrieval-Augmented Generation (RAG) has become the standard pattern for grounding LLM responses in domain-specific data. But most RAG implementations rely on simple vector similarity search over text chunks — losing the rich relationships and structure that make data meaningful.

A semantic data layer fixes this by organizing your data as an interconnected knowledge graph before it ever reaches the LLM.

The Limitations of Chunk-Based RAG

Traditional RAG works by:

1. Splitting documents into text chunks
2. Embedding those chunks as vectors
3. Retrieving the most similar chunks to a user's query
4. Passing those chunks to an LLM as context

This approach breaks down when answers require connecting information across multiple documents or understanding relationships between entities.

Building the Semantic Layer

A semantic data layer sits between your raw data and your RAG pipeline:

1. Model your domain with an ontology that defines entity types and relationships
2. Extract structured data from documents into the knowledge graph
3. Link entities across documents to build a connected graph
4. Query the graph to retrieve structured subgraphs, not just text chunks

Results from Our Benchmarks

In our LLM Accuracy Benchmark, GraphRAG approaches using a semantic data layer showed:

• 34% improvement in answer accuracy on multi-hop questions
• 28% reduction in hallucination rates
• Near-perfect provenance — every claim traceable to source data

Getting Started with Fluree

Fluree's JSON-LD native data model makes it straightforward to build a semantic data layer. Define your ontology, load your data, and query with SPARQL or FlureeQL — your RAG pipeline gets structured context instead of raw text.